lplsReg: Package for lpls regression and classification

Description Usage Arguments Value Author(s) Examples

Prediction or classification using lpls and double-cross-validation with potential jackknife variable selection and re-fitting of the lpls model to a reduced variable set based on the inner CV-loop results before prediction of samples in the outer CV-loop.

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lplsReg.dcv(X1, X2, X3, npc.sel = 1:5, alpha = 0, npc.ref = NULL,
  testlevel = 0.05, dreduce = F, colcent = c(T, T), rowcent = c(F, F),
  grandcent = c(F, F), outerfolds, innernfolds = 10, err.type = "rate")

`X1`	A response vector or matrix for regression. For classification this should be either a factor or a dummy coded 0/1 matrix with one column per group.
`X2`	Predictor matrix of size (n x p).
`X3`	Background information matrix of size (m x p)
`npc.sel`	A vector of component numbers to be tested in the initial LPLS model based on all variables in the inner CV-loop. Default is 1:5.
`alpha`	A vector of alpha-values to be tested in the initial LPLS model based on all variables in the inner CV-loop. Default is a single value 0. See `lplsReg` for details on alpha.
`npc.ref`	A vector of component numbers to be tested in the re-fitted LPLS model based on selected variables in the inner CV-loop. Default is 1:5.
`testlevel`	Testlevel for the jackknife testing of the variables. Deafult is 0.05
`dreduce`	Logical. Should variable selection on the columns of X3 (parallel to X2) also be applied to the rows of X3? This is logical only if X3 is a (p x p) matrix expressing some dependency or simlarity between the variables in X2, hence, in cases where both the rows and columns of X3 relate to the variables of X2.
`colcent`	Logical vector of length referring to X2 and X3. Should column centering be performed?
`rowcent`	Logical vector of length referring to X2 and X3. Should row centering be performed?
`grandcent`	Logical vector of length referring to X2 and X3. Should overall centering be performed?
`outerfolds`	A list of length `k` defining the sample numbers in each fold of k-fold cross-validation (outer segments). May use `balanced.folds` to make the segment list up front of the analysis.
`innernfolds`	The number of segments to be used in the inner cv-loop.
`err.type`	The evaluation criterion for prediction/classification performance. Either "rate" (total error rate), "rmsep" (root mean square error), or "rmsep2" a modified rmsep where only predictions between 0 and 1 contribute to the error. Predictions outside this range are considered as perfect predictions.

`call`	The function call
`total.error`	The total error according to the chosen evaluation criterion (see `err.type`)
`apost`	For classification: Posterior probabilities of class membership for each sample.
`trueclass`	For clasification: The true class of each sample
`class`	For clasification: The predicted class of each sample
`X1pred`	The numerical predicted value for each sample.
`cv.npc.sel`	The best number of components for the the outer-loop model chosen from the inner-loop results. This is used for the outer loop predictions if `refit = FALSE`.
`cv.alpha`	The best value of alpha for the the outer-loop model chosen from the inner-loop results.
`cv.npc.ref`	The best number of components for the the outer-loop model chosen from the results of re-fitted inner-loop models to jackknife selected variables. This is used for the outer loop predictions if `refit = TRUE`.
`varfreq`	The frequency of variables being selected across outer cross-validation segments.

Solve SÃ¦bÃ¸

data(BCdata)
segs <- balanced.folds(BCdata$Y,  5)
fit.dcv <- lplsReg.dcv(factor(BCdata$Y),  BCdata$X,  BCdata$Z,  
                       outerfolds  =  segs,  innernfolds  =  5)